Incorporating paleontological data into phylogenetic inference can greatly enrich our understanding of evolutionary relationships by providing insights into the diversity and morphological evolution of a clade over geological timescales. Phylogenetic analysis of fossil data has been significantly aided by the introduction of the fossilized birth–death (FBD) process, a model that accounts for fossil sampling through time. A decade on from the first implementation of the FBD model, we explore its use in more than 170 empirical studies, summarizing insights gained through its application. We identify a number of challenges in applying the model in practice: it requires a working knowledge of paleontological data and their complex properties, Bayesian phylogenetics, and the mechanics of evolutionary models. To address some of these difficulties, we provide an introduction to the Bayesian phylogenetic framework, discuss important aspects of paleontological data, and finally describe the assumptions of the models used in paleobiology. We also present a number of exemplar empirical studies that have used the FBD model in different ways. Through this review, we aim to provide clarity on how paleontological data can best be used in phylogenetic inference. We hope to encourage communication between model developers and empirical researchers, with the ultimate goal of developing models that better reflect the data we have and the processes that generated them.

Data compilations expand the scope of research; however, data citation practice lags behind advances in data use. It remains uncommon for data users to credit data producers in professionally meaningful ways. In paleontology, databases like the Paleobiology Database (PBDB) enable assessment of patterns and processes spanning millions of years, up to global scale. The status quo for data citation creates an imbalance wherein publications drawing data from the PBDB receive significantly more citations (median: 4.3 ± 3.5 citations/year) than the publications producing the data (1.4 ± 1.3 citations/year). By accounting for data reuse where citations were neglected, the projected citation rate for data-provisioning publications approached parity (4.2 ± 2.2 citations/year) and the impact factor of paleontological journals (n = 55) increased by an average of 13.4% (maximum increase = 57.8%) in 2019. Without rebalancing the distribution of scientific credit, emerging “big data” research in paleontology—and science in general—is at risk of undercutting itself through a systematic devaluation of the work that is foundational to the discipline.

With the aim of producing a 3D representation of tumors, imaging and molecular annotation of xenografts and tumors (IMAXT) uses a large variety of modalities in order to acquire tumor samples and produce a map of every cell in the tumor and its host environment. With the large volume and variety of data produced in the project, we developed automatic data workflows and analysis pipelines. We introduce a research methodology where scientists connect to a cloud environment to perform analysis close to where data are located, instead of bringing data to their local computers. Here, we present the data and analysis infrastructure, discuss the unique computational challenges and describe the analysis chains developed and deployed to generate molecularly annotated tumor models. Registration is achieved by use of a novel technique involving spherical fiducial marks that are visible in all imaging modalities used within IMAXT. The automatic pipelines are highly optimized and allow to obtain processed datasets several times quicker than current solutions narrowing the gap between data acquisition and scientific exploitation.